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Stata's expertise lies in the analysis of time based data. Stata provides not only the basic time series models like ARIMA but even the multivariate equivalents (VAR/VEC-Models) as well. Further you are able to model volatility using GARCH-models in Stata. Kaplan-Meier-curves are the way to analyse survival times, while mixed models help to analyse panel data. A mighty scripting language completes the package.

Stata produces all kinds of classical statistics. You can use it for descriptive statistics, hypothesis testing and visualization of data. Typically Stata is used in research and development. The large amount of different statistical methods helps scientists in all fields of applications (Social science, econometrics, epidimiology, medical research).

No matter if you are a student or a senior researcher, there is always the right version of Stata available: Stata/IC, Stata/SE and Stata/MP

Arguments for Stata:

  • Used in research and development
  • Wide range of statistical and graphical methods
  • Comprehensive statistical software
  • Flexible and especially powerful for analysis of time series
  • Easy to learn but mighty scripting language

Recommended products

Stata 15/SE

Stata 15/SE

Stata 15/SE

A comprehensive statistics program for time series analysis! more details

Download pricelist Product information

STATA 15/MP

STATA 15/MP

STATA 15/MP

A comprehensive statistics program for time series analysis! more details

Download pricelist Product information

Stata 15 - Student License

Stata 15 - Student License

Stata 15 - Student License

A comprehensive statistics program for time series analysis! more details

Download pricelist Product information

Stata/IC

Stata statistical software is a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics. Stata is not sold in modules, which means you get everything you need in one package.

Easy to learn yet fully programmable for the most demanding data management and statistical requirements.

With Stata's menus and dialogs, you can easily point and click or drag and drop your way to all of Stata's statistical, graphical, and data management features. You can completely reshape your data, create group-level variables for panel or longitudinal data, graph a receiver operating characteristics (ROC) curve or impulse-response function (IRF), perform a case-control analysis, estimate a random-effects count-data model or a Cox proportional hazards model, or compute marginal effects from a nonlinear estimator. You can even access the dialog boxes for each command directly from the online help system. T his is a great way to explore all of the capabilities of Stata.

Stata Software is available in 3 different flavors

Whether you’re a student or a seasoned research professional, we have a package designed to suit your needs:

  • Stata/MP: The fastest version of Stata (for quad-core, dual-core, and multicore/multiprocessor computers) that can analyze the most data
  • Stata/SE: Stata for large datasets
  • Stata/IC: Stata for mid-sized datasets
  • Numerics by Stata: Stata for embedded and web applications

Stata/MP is the fastest and largest version of Stata. Virtually any current computer can take advantage of the advanced multiprocessing of Stata/MP. This includes the Intel i3, i5, i7, i9, Xeon, and Celeron, and AMD multi-core chips. On dual-core chips, Stata/MP runs 40% faster overall and 72% faster where it matters, on the time-consuming estimation commands. With more than two cores or processors, Stata/MP is even faster. Find out more about Stata/MP.

Stata/MP, Stata/SE, and Stata/IC all run on any machine, but Stata/MP runs faster. You can purchase a Stata/MP license for up to the number of cores on your machine (maximum is 64). For example, if your machine has eight cores, you can purchase a Stata/MP license for eight cores, four cores, or two cores.

Stata/MP can also analyze more data than any other flavor of Stata. Stata/MP can analyze 10 to 20 billion observations given the current largest computers, and is ready to analyze up to 1 trillion observations once computer hardware catches up.

Stata/SE and Stata/IC differ only in the dataset size that each can analyze. Stata/SE and Stata/MP can fit models with more independent variables than Stata/IC (up to 10,998). Stata/SE can analyze up to 2 billion observations.

Stata/IC allows datasets with as many as 2,048 variables and 2 billion observations. Stata/IC can have at most 798 independent variables in a model.

Numerics by Stata can support any of the data sizes listed above in an embedded environment.

All the above flavors have the same complete set of features and include PDF documentation.

Product features  Stata/IC  Stata/SE  Stata/MP
Maximum number of variables  2,048 32,767 120
Maximum number of observations 2.14 billion  2.14 billion  Up to 20 billion
Maximum number of independent variables  798 10,998 10,998
Multicore support (Time to run logistic regression with 5 million obs and 10 covariates ) 1-core/ 10.0 sec 1-core/ 10.0 sec 2- core (5.0 sec),    4-core (2,6 sec), 4+ core (even faster)
Complete suite of statistical features  Yes!  Yes!  Yes!
Publication-quality graphics  Yes!  Yes!  Yes!
Matrix programming language  Yes!  Yes!  Yes!
Complete PDF documentation  Yes!  Yes!  Yes!
Exceptional technical support  Yes!  Yes!  Yes!
Includes within-release updates  Yes!  Yes!  Yes!
64-bit version available  Yes!  Yes!  Yes!
Windows, macOS, and Linux  Yes!  Yes!  Yes!
Memory requirements  1 GB  2 GB  4 GB
Disk space requirements  1 GB  1 GB  1 GB 

 

* The maximum number of observations is limited only by the amount of available RAM on your system.

Stata scripting language

Stata's scripting language is easy to learn and helps you to get the most out of your data. It allows not only to use and modify the existing routines to generate standard reports, but can easily be extended with newly created statistical functions.

Efficient Datamanagent with Stata

Datamanagement with Stata is easy and efficient. Joining datasets, creating new variables or producing summary tables is done in no time.

Professional Graphics with Stata

Stata provides professional graphics that can directly be used for documents and publications. This includes not only pre-defined standard graphs but although highly customizeable graphics.

Further Information:

https://www.stata.com/why-use-stata/

 

Trialversion of Stata

The producer provides a free 30-day trialversion on their website. The trialversion contains all the features of Stata. You can register for this license simply by visiting the following link: http://www.stata.com/customer-service/evaluate-stata/

 

 

Windows Mac Linux
Hardware requirements

Package                 Memory            Disk space
Stata/MP                  4 GB                   1 GB
Stata/SE                   2 GB                   1 GB
Stata/IC                   1 GB                   1 GB
 

Package                 Memory            Disk space
Stata/MP                  4 GB                   1 GB
Stata/SE                   2 GB                   1 GB
Stata/IC                   1 GB                   1 GB
 

Package                 Memory            Disk space
Stata/MP                  4 GB                   1 GB
Stata/SE                   2 GB                   1 GB
Stata/IC                   1 GB                   1 GB
 

Stata for Unix requires a video card that can display thousands of colors or more (16-bit or 24-bit color)

Operating System
  • Windows 10 *
  • Windows 8 *
  • Windows 7 *
  • Windows Vista *
  • Windows Server 2016, 2012, 2008, 2003
  •  
  • 64-bit and 32-bit Windows varieties for x86-64 and x86 processors made by Intel® and AMD
Stata for macOS requires 64-bit Intel® processors (Core™2 Duo or better) running macOS 10.9 or newer
  • Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running Linux
  • For xstata, you need to have GTK 2.24 installed

 

 

 

 

 

New in Stata 15

 

Latent class analysis (LCA)

lca diagram

Discover and understand the unobserved groupings in your data. Use LCA's model-based classification to find out

  • how many groups you have,
  • who is in those groups, and
  • what makes those groups distinct.
 
 

bayes: logistic ...
and 44 more

Bayesian regressionPlatforms
Stata for Windows®
• Windows 10 *
• Windows 8 *
• Windows 7 *
• Windows Vista *
• Windows Server 2016, 2012, 2008, 2003 *
* 64-bit and 32-bit Windows varieties for x86-64 and x86 processors made by Intel® and AMD Find out if your OS is 64-bit compliant.
Stata for Mac®
• Stata for macOS requires 64-bit Intel® processors (Core™2 Duo or better) running macOS 10.9 or newer Find out if your OS is 64-bit compliant.
Stata for Linux
• Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running Linux
• For xstata, you need to have GTK 2.24 installed
Hardware requirements
Package
Memory
Disk space Stata/MP 4 GB 1 GB Stata/SE 2 GB 1 GB Stata/IC 1 GB 1 GB
Stata for Unix requires a video card that can display thousands of colors or more (16-bit or 24-bit color)

Type bayes: in front of any of 45 Stata estimation commands to fit a Bayesian regression model.

 
 

Markdown & dynamic documents

Type this,
code
Get this,
markdown thumbnail
  • Create webpages from Stata
  • Intermix text, regressions, results, graphs, etc.
  • See changes in data or commands automatically reflected on webpage
 
 

Linearized DSGEs

Write your model in simple algebraic form. Stata does the rest: solve model, estimate parameters, estimate policy and transition matrices (with CIs), estimate and graph IRFs, and perform forecasts.

 
 

Finite mixture models (FMMs)

  • 17 estimators and combinations
  • Continuous, binary, count, ordinal, categorical, censored, and truncated outcomes
  • Survival outcomes
 
 

Spatial autoregressive models

Because


         sometimes


                  where you are


                                  matters.
 
 

Interval-censored survival models

graph

Fit any of Stata's six parametric survival models to interval-censored data. All the usual survival features are supported: stratified estimation, robust and clustered SEs, survey data, graphs, and more.

 
 

Nonlinear multilevel
mixed-effects models

      When ...
      your science ...
      says ...
      your model ...
      is ...
      nonlinear in its parameters
 
 

Mixed logit models: Advanced choice modeling

Do you walk to work, ride a bus, or drive your car? Which of three insurance plans do you buy? Which political party do you vote for?

We make dozens of choices every day. Researchers have access to gaggles of data about those choices. Mixed logit introduces random effects into choice modeling and thereby relaxes the IIA assumption and increases model flexibility.

 
 

Nonparametric regression

When you know something matters.

But have no idea how.

 
 

Create Word documents from Stata

  • Automate your reports
  • Write paragraphs and tables to Word documents
  • Embed Stata results and graphs in paragraphs and tables
  • Customize formatting of text, tables, and cells
 
PDF Create PDFs, too!
 

Bayesian multilevel models

Graph

Small number of groups?
Many hierarchical levels?
Prefer making probability statements?

Consider Bayesian multilevel modeling.

 
 

Threshold regression

graph

Your time-series regression may change parameters at some point in time or at multiple points in time. The activity of foraging animals might follow a completely different pattern at temperatures above some threshold. You may not know the value of that threshold. Finding such thresholds and estimating the parameters within the regimes is what threshold regression does.

 
 

Panel-data tobit with random coefficients

graph

Stata has long had estimators for random effects (random intercepts) in panel data.

 

Search, browse, and import FRED data

graph

The St. Louis Federal Reserve makes available over 470,000 U.S. and international economic and financial time series. You can now easily search, browse, and import these data.

 
 

Multilevel regression for interval-measured outcomes

Incomes are sometimes recorded in groupings, as are people's weights, insect counts, grade-point averages, and hundreds of other measures. Often we have repeated measurements for individuals, or schools, or orchards, etc. So ... we need multilevel regression for interval-measured (interval-censored) outcomes.

 
 

Multilevel tobit regression for censored outcomes

  • Left-censoring, right-censoring, both
  • Censoring that varies by observation
  • Make inferences about either the uncensored or the censored outcome
  • Robust and clustered SEs
  • Support for survey data
 
 

Panel-data cointegration tests

graph
  • Tests
    • Kao
    • Pedroni
    • Westerlund
  • Total of nine variants of tests
 
 

Tests for multiple breaks in time series

graph
  • Cumulative sum (CUSUM) test for parameter stability
    • CUSUM of recursive residuals
    • CUSUM of OLS residuals
  • Plots with CIs
 
 

Multiple-group generalized SEM

graph graph graph

Generalized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include

  • continuous, binary, ordinal, count, categorical, and even survival outcomes
  • multilevel models
 
 

ICD-10-CM/PCS

  • NCHS's ICD-10-CM diagnosis codes
  • CMS's ICD-10-PCS procedure codes
  • Verify codes are valid
  • Create new variables based on codes
 
 

Power for cluster randomized designs

power twomeans graph

Power analysis for comparing

  • One- and two-sample means
  • One- and two-sample proportions
  • Two-sample survivor curves

when you randomize clusters instead of individuals

 
 

Power for linear regression models

screenshot of dialog box
  • Solve for
    • Power
    • Sample size
    • Effect size
  • Specify lists of
    • Alpha values
    • Power levels
    • Beta values
    • Sample sizes
    • And more
  • Automated tables and graphs
 
 

Heteroskedastic linear regression

graph
  • Model for the variance
  • Robust and cluster SEs
  • Survey-data support
 
 

Poisson models with sample selection

Counts are common. How many:

Fish did you catch?
Accidents occurred?
Patents does a firm generate?

Outcomes are not always seen.

Folks evade the game warden.
Accidents are not always reported.
Some firms prefer trade secrets to patents.

So you need Poisson models with sample selection.

 
 

More in panel data

Nonlinear models with random effects, including random coefficients Bayesian panel-data models Interval regression with random intercepts and random coefficients
 

More in graphics

graph Transparency in graphs SVG export
 

More in statistics

Bayesian survival models Zero-inflated ordered probit Add your own power and sample-size methods Bayesian sample-selection models And yet more
 

More in the interface

Stata in Swedish
Stata in Chinese
Improvements to the Do-file Editor
 

And, even more

Stream random-number generator Improvements for Java plugins

 

The whole feature list you will find under the following link:

https://www.stata.com/features/

 

Stata Features

Data management

data transformations, match-merge, ODBC, XML, by-group processing, append files, sort, row–column transposition, labeling, saving results

Basic statistics

summaries, cross-tabulations, correlations, t tests, equality-of-variance tests, tests of proportions, confidence intervals, factor variables

Linear models

regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; three-stage least squares; constraints; quantile regression; GLS

Multilevel mixed-effects models

generalized linear models;continuous, binary, and count outcomes; two-, three-, and higher-level models; random-intercepts; random-slopes; crossed random effects; BLUPs of effects and fitted values; hierarchical models; residual error structures; support for survey data in linear models

Binary, count, and discrete outcomes

logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic; multinomial probit; zero-inflated and left-truncated count models; selection models; marginal effects

Longitudinal data/panel data

random and fixed effects with robust standard errors; linear mixed models, random-effects probit, GEE, random- and fixed-effects Poisson, dynamic panel-data models, and instrumental-variables regression; panel unit-root tests; AR(1) disturbances

Generalized linear models (GLMs)

ten link functions, user-defined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals

Nonparametric methods

Wilcoxon-Mann-Whitney, Wilcoxon signed ranks and Kruskal-Wallis tests; Spearman and Kendall correlations; Kolmogorov-Smirnov tests; exact binomial CIs; survival data; ROC analysis; smoothing; bootstrapping

Exact statistics

exact logistic and Poisson regression, exact case-control statistics, binomial tests, Fisher's exact test for r × c tables

ANOVA/MANOVA

balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; contrasts

Multivariate methods

factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, user-extensible analyses

Cluster analysis

hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; user-extensible analyses

Resampling and simulation methods

bootstrapping, jackknife and Monte Carlo simulation; permutation tests

Tests, predictions, and effects

Wald tests; LR tests; linear and nonlinear combinations, predictions and generalized predictions, marginal means, least-squares means, adjusted means; marginal and partial effects; forecast models; Hausman tests

Graphics

line charts, scatterplots, bar charts, pie charts, hi-lo charts, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution Q-Q plots

Survey methods

multistage designs; bootstrap, BRR, jackknife, linearized, and SDR variance estimation; poststratification; DEFF; predictive margins; means, proportions, ratios, totals; summary tables; regression, instrumental variables, probit, Cox regression

Survival analysis

Kaplan-Meier and Nelson-Aalen estimators,; Cox regression (frailty); parametric models (frailty); competing risks; hazards; time-varying covariates; left- and right-censoring, Weibull, exponential, and Gompertz analysis

Epidemiology

standardization of rates, case–control, cohort, matched case-control, Mantel-Haenszel, pharmacokinetics, ROC analysis, ICD-9-CM

Time series

ARIMA; ARFIMA; ARCH/GARCH; VAR; VECM; multivariate GARCH; unobserved components model; dynamic factors; state-space models; business calendars; correlograms; periodograms; forecasts; impulse-response functions; unit-root tests; filters and smoothers; rolling and recursive estimation

Multiple imputation

nine univariate imputation methods; multivariate normal imputation; chained equations; explore pattern of missingness; manage imputed datasets; fit model and pool results; transform parameters; joint tests of parameter estimates; predictions

Simple maximum likelihood

specify likelihood using simple expressions; no programming required; survey data; standard, robust, bootstrap, and jackknife SEs; matrix estimators

Programmable maximum likelihood

user-specified functions; NR, DFP, BFGS, BHHH; OIM, OPG, robust, bootstrap, and jackknife SEs; Wald tests; survey data; numeric or analytic derivatives

Other statistical methods

kappa measure of interrater agreement; Cronbach's alpha; stepwise regression; tests of normality

Programming features

adding new commands; command scripting; object-oriented programming; menu and dialog-box programming; Project Manager; plugins

Matrix programming-Mata

interactive sessions, large-scale development projects, optimization, matrix inversions, decompositions, eigenvalues and eigenvectors, LAPACK engine, real and complex numbers, string matrices, interface to Stata datasets and matrices, numerical derivatives, object-oriented programming

Internet capabilities

ability to install new commands, web updating, web file sharing, latest Stata news

Accessibility

Section 508 compliance, accessibility for persons with disabilities

Sample session

A sample session of Stata for Mac, Unix, or Windows.

Community-contributed commands

User-written commands for meta-analysis, data management, survival, econometrics

Graphical user interface

menus and dialogs for all features; Data Editor; Variables Manager; Graph Editor; Project Manager; Do-file Editor; Clipboard Preview Tool; multiple preference sets

Graphics

line charts; scatterplots; bar charts; pie charts; hi-lo charts; contour plots; GUI Editor; regression diagnostic graphs; survival plots; nonparametric smoothers; distribution Q-Q plots

Documentation

20 manuals20 manuals; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references

Power and sample size

power; sample size; effect size; minimum detectable effect; means; proportions; variances; correlations; case-control studies; cohort studies; survival analysis; balanced or unbalanced designs; results in tables or graphs

Treatment effects

inverse probability weight (IPW); doubly robust methods; propensity score matching; regression adjustment; covariate matching; multilevel treatments; average treatment effects (ATEs); average treatment effects on the treated (ATETs); potential-outcome means (POMs)

SEM (Structural equation modeling)

graphical path diagram builder; standardized and unstandardized estimates; modification indices; direct and indirect effects; continuous, binary, count, and ordinal outcomes (GLM); multilevel models; random slopes and intercepts; factors scores, empirical Bayes, and other predictions; groups and tests of invariance; goodness of fit; handles MAR data by FIML; correlated data

Functions

statistical; random-number; mathematical; string; date and time

Embedded statistical computations

Numerics by Stata

Contrasts, pairwise comparisons, and margins

compare means, intercepts, or slopes; compare to reference category, adjacent category, grand mean, etc.; orthogonal polynomials; multiple comparison adjustments; graph estimated means and contrasts; interaction plots

GMM an nonlinear regression

generalized method of moments (GMM); nonlinear regression